Large-Break Loss-of-Coolant Accident Phenomena Identification and Ranking Table (PIRT) for the Advanced CANDU Reactor
Bibliographic record
Abstract
The Advanced CANDU Reactor (ACR™)* is an evolutionary advancement of the current CANDU 6® reactor, aimed at producing electrical power for a capital cost and unit-energy cost significantly less than that of current reactor designs. The ACR retains the modular concept of horizontal fuel channels surrounded by heavy water moderator, as with all CANDU reactors. However, ACR uses slightly enriched uranium (SEU) fuel, compared to the natural uranium used in CANDU 6. This achieves the twin goals of improved economics (e.g., via reductions in the heavy water requirements and the use of a light water coolant), as well as improved safety. This paper is focused on the double-ended guillotine critical inlet header break (CRIHB) loss-of-coolant accident (LOCA) in an ACR reactor, which is considered as a large break LOCA. Large Break LOCA in water-cooled reactors has been used historically as a design basis event by regulators, and it has attracted a very large share of safety analysis and regulatory review. The LBLOCA event covers a wide range of system behaviours and fundamental phenomena. The Phenomena Identification and Ranking Table (PIRT) for LBLOCA therefore provides a good understanding of many of the safety characteristics of the ACR design. The paper outlines the design characteristics of the ACR reactor that impact the PIRT process and computer code applicability. It also describes the LOCA phenomena, lists all components and systems that have an important role during the event, discusses the PIRT process and results, and presents the final PIRT summary table.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".